{"id":"W3176509696","doi":"10.1155/2021/8830561","title":"Research on Hub-and-Spoke Transportation Network of China Railway Express","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Education of the People's Republic of China; China Railway","keywords":"Port (circuit theory); Spoke-hub distribution paradigm; Node (physics); Lagrangian relaxation; Mode (computer interface); Construct (python library); Transport engineering; Heuristic; Key (lock); Operations research; Flow network; Point (geometry); Dual (grammatical number); Network model; Computer science; Engineering; Mathematical optimization; Computer network; Mathematics; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001234508,0.0001133557,0.0002858733,0.0001633536,0.0003083901,0.00002830315,0.0001221079,0.0001268393,0.00007245639],"category_scores_gemma":[0.00008755912,0.0001153102,0.0001163727,0.0007832081,0.0001552856,0.0005630082,4.894376e-7,0.0003503502,9.470849e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004585526,"about_ca_system_score_gemma":0.0003042117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007136528,"about_ca_topic_score_gemma":0.0009314833,"domain_scores_codex":[0.9976208,0.0002401987,0.0007462322,0.000191481,0.0009405157,0.0002607876],"domain_scores_gemma":[0.9978482,0.000287738,0.0004910293,0.0001061328,0.001128965,0.0001379648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0007102404,0.0002009256,0.01413119,0.00009933273,0.00006209849,0.0001147862,0.04540807,0.8906804,0.002438972,0.03734826,0.0003265813,0.008479206],"study_design_scores_gemma":[0.001967935,0.0003876337,0.9570631,0.0006298627,0.0001042959,0.000002948523,0.01754383,0.00004050856,0.003686846,0.004978854,0.01336683,0.0002273904],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9866079,0.0006853357,0.01000868,0.0008240619,0.0005485019,0.0001740494,0.00003678988,0.0000242844,0.001090365],"genre_scores_gemma":[0.9871613,0.001525668,0.01067496,0.00003747709,0.0002141311,0.000004736154,0.0001195157,0.00001676273,0.0002454129],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9429319,"threshold_uncertainty_score":0.4702213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02962058897847384,"score_gpt":0.350190186468833,"score_spread":0.3205695974903591,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}